Heterogeneous Medical Knowledge Graph Completion using GNN for inductive Link prediction and Medicine Recommendation

In the biomedical realm, a Knowledge graph is something that combines different sources of information that are expert - driven into a graph, in which, nodes represent biomedical entities like diseases, genomes, symptoms, drugs etc. Relationship between various entities are represented by edges. This paper focuses on creating a heterogeneous medical knowledge graph and predicting missing relations between symptom to drug, medicine recommendation using Graph Neural Networks(GNN) and Graphsage networks. This paper aims to create a Medical Knowledge Graph based on multi modal data (text data, chemical structure, etc.) on diseases, symptoms and drugs, then use a Graph Neural Network and its variants to predict the missing links between symptoms and drugs and also use medicine recommendation and then compare with existing methods and highlight the advantages of proposed methodology including evaluating the model with existing algorithms. In this paper, we were able to construct a heterogeneous knowledge graph consisting of disease, symptoms and drugs as nodes. We were able to achieve an accuracy and prediction above 80 percentage, which was higher than current existing models including translational distance models, DistMult , etc. With the model, we were able to predict missing links between symptoms and drugs, which could be more helpful in research and also able to recommend medicine, given a set of symptoms.